SoroushVahidi/scidocs huggingface dataset:
A New Framework for Computer Science and Engineering
(2004)

T raditionally, computing studies has two partitions—science and engineering—that are separated by a line roughly at the computer architecture level. Above the line is computer science and below is computer engineering. Each of these two disciplines then breaks into subspecialties, such as the decomposition of computer science into theory, systems, and AI. Such an approach succeeds in dividing the field into affinity groups, but it tends to isolate its respective parts and the systems that come from them. This isolation, in turn, makes it difficult to incorporate either an integrative systems-oriented perspective or the interdisciplinary work that is rapidly becoming critical to computing studies’ future.1 A more effective organization for computer science and engineering (CS&E)—and one that helps point the way toward its promising future—requires an intrinsically interdisciplinary framework that combines academic and systems-oriented computing perspectives. My colleagues and I have been developing such a framework, which is already changing key segments of CS&E at the University of Southern California (USC). The framework reaggregates computer science and computer engineering and then repartitions the resulting single field into analysis and synthesis components.2-4 Analysis, the more academic of the two, focuses on the nature of computing and the relationships between computer science and other major scientific domains. The synthesis component builds up computer engineering as a more pragmatic systems-oriented activity through a hierarchy of related layers, each of which covers important interdisciplinary issues. The framework is based on the notion that science is foremost about dissecting and understanding, and engineering is mostly about envisioning and building. True, scientists also envision and build—integrated models, for example—but such activities are directed mostly at dissecting and understanding something. Likewise engineers also dissect and understand things, but primarily to envision and build. Extending these notions to computing studies, computer science is concerned with dissecting and understanding the nature of computing and its relationship to the other sciences, while computer engineering is concerned To realize the compelling future of computing studies, researchers and analysts must rethink the traditional way of partitioning and structuring computer science and engineering. A framework that USC is already partially implementing combines an explicitly interdisciplinary academic focus with a systems-oriented perspective. Paul S. Rosenbloom University of Southern California
Vision-based automated parking system
(2010)

This paper describes an approach to overcome a situation of monitoring and managing a parking area using a vision based automated parking system. With the rapid increase of cars the need to find available parking space in the most efficient manner, to avoid traffic congestion in a parking area, is becoming a necessity in car park management. Current car park management is dependent on either human personnel keeping track of the available car park spaces or a sensor based system that monitors the availability of each car park space or the overall number of available car park spaces. In both situations, the information available was only the total number of car park spaces available and not the actual location available. In addition, the installation and maintenance cost of a sensor based system is dependent on the number of sensors used in a car park. This paper shows a vision based system that is able to detect and indicate the available parking spaces in a car park. The methods utilized to detect available car park spaces were based on coordinates to indicate the regions of interest and a car classifier. This paper shows that the initial work done here has an accuracy that ranges from 90% to 100% for a 4 space car park. The work done indicated that the application of a vision based car park management system would be able to detect and indicate the available car park spaces
Automatic Classification for Vietnamese News
(2015)

This paper proposes an automatic framework to classify Vietnamese news from news sites on the Internet. In this proposed framework, the extracted main content of Vietnamese news is performed automatically by applying the improved performance extraction method from [1]. This information will be classified by using two machine learning methods: Support vector machine and naïve bayesian method. Our experiments implemented with Vietnamese news extracted from some sites showed that the proposed classification framework give acceptable results with a rather high accuracy, leading to applying it to real information systems.
Co-change Clusters: Extraction and Application on Assessing Software Modularity
(2015)

The traditional modular structure defined by the package hierarchy suffers from the dominant decomposition problem and it is widely accepted that alternative forms of modularization are necessary to increase developer’s productivity. In this paper, we propose an alternative form to understand and assess package modularity based on co-change clusters, which are highly inter-related classes considering cochange relations. We evaluate how co-change clusters relate to the package decomposition of four real-world systems. The results show that the projection of co-change clusters to packages follows different patterns in each system. Therefore, we claim that modular views based on co-change clusters can improve developers’ understanding on how well-modularized are their systems, considering that modularity is the ability to confine changes and evolve components in parallel.
The Sensemaking-Coevolution-Implementation Theory of software design
(2015)

Understanding software design practice is critical to understanding modern information systems development. New developments in empirical software engineering, information systems design science and the interdisciplinary design literature combined with recent advances in process theory and testability have created a situation ripe for innovation. Consequently, this paper utilizes these breakthroughs to formulate a process theory of software design practice: SensemakingCoevolution-Implementation Theory explains how complex software systems are created by collocated software development teams in organizations. It posits that an independent agent (design team) creates a software system by alternating between three activities: organizing their perceptions about the context, mutually refining their understandings of the context and design space, and manifesting their understanding of the design space in a technological artifact. This theory development paper defines and illustrates Sensemaking-Coevolution-Implementation Theory, grounds its concepts and relationships in existing literature, conceptually evaluates the theory and situates it in the broader context of information systems development.
Bayesian Nonparametric Modeling of Categorical Data for Information Fusion and Causal Inference
(2018)

This paper presents a nonparametric regression model of categorical time series in the setting of conditional tensor factorization and Bayes network. The underlying algorithms are developed to provide a flexible and parsimonious representation for fusion of correlated information from heterogeneous sources, which can be used to improve the performance of prediction tasks and infer the causal relationship between key variables. The proposed method is first illustrated by numerical simulation and then validated with two real-world datasets: (1) experimental data, collected from a swirl-stabilized lean-premixed laboratory-scale combustor, for detection of thermoacoustic instabilities and (2) publicly available economics data for causal inference-making.
Classification of watermelon leaf diseases using neural network analysis
(2013)

This paper mainly discussed the process to classify Anthracnose and Downey Mildew, watermelon leaf diseases using neural network analysis. A few of infected leaf samples were collected and they were captured using a digital camera with specific calibration procedure under controlled environment. The classification on the watermelon's leaf diseases is based on color feature extraction from RGB color model where the RGB pixel color indices have been extracted from the identified Regions of Interest (ROI). The proposed automated classification model involved the process of diseases classification using Statistical Package for the Social Sciences (SPSS) and Neural Network Pattern Recognition Toolbox in MATLAB. Determinations in this work have shown that the type of leaf diseases achieved 75.9% of accuracy based on its RGB mean color component.
Human Interaction With Robot Swarms: A Survey
(2016)

Recent advances in technology are delivering robots of reduced size and cost. A natural outgrowth of these advances are systems comprised of large numbers of robots that collaborate autonomously in diverse applications. Research on effective autonomous control of such systems, commonly called swarms, has increased dramatically in recent years and received attention from many domains, such as bioinspired robotics and control theory. These kinds of distributed systems present novel challenges for the effective integration of human supervisors, operators, and teammates that are only beginning to be addressed. This paper is the first survey of human-swarm interaction (HSI) and identifies the core concepts needed to design a human-swarm system. We first present the basics of swarm robotics. Then, we introduce HSI from the perspective of a human operator by discussing the cognitive complexity of solving tasks with swarm systems. Next, we introduce the interface between swarm and operator and identify challenges and solutions relating to human-swarm communication, state estimation and visualization, and human control of swarms. For the latter, we develop a taxonomy of control methods that enable operators to control swarms effectively. Finally, we synthesize the results to highlight remaining challenges, unanswered questions, and open problems for HSI, as well as how to address them in future works.
A k-mean clustering algorithm for mixed numeric and categorical data
(2007)

Use of traditional k-mean type algorithm is limited to numeric data. This paper presents a clustering algorithm based on k-mean paradigm that works well for data with mixed numeric and categorical features. We propose new cost function and distance measure based on co-occurrence of values. The measures also take into account the significance of an attribute towards the clustering process. We present a modified description of cluster center to overcome the numeric data only limitation of k-mean algorithm and provide a better characterization of clusters. The performance of this algorithm has been studied on real world data sets. Comparisons with other clustering algorithms illustrate the effectiveness of this approach. 2007 Elsevier B.V. All rights reserved.
Unsupervised Learning with Mixed Numeric and Nominal Data
(2002)

ÐThis paper presents a Similarity-Based Agglomerative Clustering (SBAC) algorithm that works well for data with mixed numeric and nominal features. A similarity measure, proposed by Goodall for biological taxonomy [15], that gives greater weight to uncommon feature value matches in similarity computations and makes no assumptions of the underlying distributions of the feature values, is adopted to define the similarity measure between pairs of objects. An agglomerative algorithm is employed to construct a dendrogram and a simple distinctness heuristic is used to extract a partition of the data. The performance of SBAC has been studied on real and artificially generated data sets. Results demonstrate the effectiveness of this algorithm in unsupervised discovery tasks. Comparisons with other clustering schemes illustrate the superior performance of this approach. Index TermsÐAgglomerative clustering, conceptual clustering, feature weighting, interpretation, knowledge discovery, mixed numeric and nominal data, similarity measures, 2 aggregation.
Chameleon: Hierarchical Clustering Using Dynamic Modeling
(1999)

68 Computer C lustering is a discovery process in data mining. 1 It groups a set of data in a way that maximizes the similarity within clusters and minimizes the similarity between two different clusters. 1,2 These discovered clusters can help explain the characteristics of the underlying data distribution and serve as the foundation for other data mining and analysis techniques. Clustering is useful in characterizing customer groups based on purchasing patterns, categorizing Web documents, 3 grouping genes and proteins that have similar func-tionality, 4 grouping spatial locations prone to earthquakes based on seismological data, and so on. Most existing clustering algorithms find clusters that fit some static model. Although effective in some cases, these algorithms can break down—that is, cluster the data incorrectly—if the user doesn't select appropriate static-model parameters. Or sometimes the model cannot adequately capture the clusters' characteristics. Most of these algorithms break down when the data contains clusters of diverse shapes, densities , and sizes. Existing algorithms use a static model of the clusters and do not use information about the nature of individual clusters as they are merged. Furthermore, one set of schemes (the CURE algorithm and related schemes) ignores the information about the aggregate interconnectivity of items in two clusters. The other set of schemes (the Rock algorithm, group averaging method, and related schemes) ignores information about the closeness of two clusters as defined by the similarity of the closest items across two clusters. (For more information, see the " Limitations of Traditional Clustering Algorithms " sidebar.) By only considering either interconnectivity or close-ness, these algorithms can easily select and merge the wrong pair of clusters. For instance, an algorithm that focuses only on the closeness of two clusters will incorrectly merge the clusters in Figure 1a over those in Figure 1b. Similarly, an algorithm that focuses only on interconnectivity will, in Figure 2, incorrectly merge the dark-blue with the red cluster rather than the green one. Here, we assume that the aggregate interconnec-tivity between the items in the dark-blue and red clusters is greater than that of the dark-blue and green clusters. However, the border points of the dark-blue cluster are much closer to those of the green cluster than those of the red cluster. Chameleon is a new agglomerative hierarchical clustering algorithm that overcomes the limitations of existing clustering algorithms. Figure 3 (on page 70) provides an overview of the overall approach …
Cortical cores in network dynamics
(2018)

Spontaneous brain activity at rest is spatially and temporally organized in networks of cortical and subcortical regions specialized for different functional domains. Even though brain networks were first studied individually through functional Magnetic Resonance Imaging, more recent studies focused on their dynamic 'integration'. Integration depends on two fundamental properties: the structural topology of brain networks and the dynamics of functional connectivity. In this scenario, cortical hub regions, that are central regions highly connected with other areas of the brain, play a fundamental role in serving as way stations for network traffic. In this review, we focus on the functional organization of a set of hub areas that we define as the 'dynamic core'. In the resting state, these regions dynamically interact with other regions of the brain linking multiple networks. First, we introduce and compare the statistical measures used for detecting hubs. Second, we discuss their identification based on different methods (functional Magnetic Resonance Imaging, Diffusion Weighted Imaging, Electro/Magneto Encephalography). Third, we show that the degree of interaction between these core regions and the rest of the brain varies over time, indicating that their centrality is not stationary. Moreover, alternating periods of strong and weak centrality of the core relate to periods of strong and weak global efficiency in the brain. These results indicate that information processing in the brain is not stable, but fluctuates and its temporal and spectral properties are discussed. In particular, the hypothesis of 'pulsed' information processing, discovered in the slow temporal scale, is explored for signals at higher temporal resolution.
Machine Learning Approach for Intrusion Detection on Cloud Virtual Machines
(2013)

Development of the cloud computing in recent years is increasing rapidly and gained great success, its security issues have got more and more attention. Many challenges in cloud computation increase the threat of data and service availability. There is need of many security services in order to improve cloud security for users as well as providers. In this paper, we propose a Anomaly Intrusion Detection System using machine learning approach for virtual machines on cloud computing. Our proposal is feature selection over events from Virtual Machine Monitor to detect anomaly in parallel to training the system so it will learn new threats and update the model. The experiment has been carried out on NSL-KDD'99 datasets using Naïve Bayes Tree (NB Tree) Classifier and hybrid approach of NB Tree and Random Forest.
An Ontology for Secure Socio-Technical Systems
(2007)

Security is often compromised by exploiting vulnerabilities in the interface between the organization and the information systems that support it. This reveals the necessity of modeling and analyzing information systems together with the organizational setting where they will operate. In this chapter we address this problem by presenting a modeling language tailored to analyze the problem of security at an organizational level. This language proposes a set of concepts founded on the notions of permission, delegation, and trust. The chapter also presents a semantics for these concepts, based on Datalog. A case study from the bank domain is employed to illustrate the proposed language.
Evaluating operating system vulnerability to memory errors
(2012)

Reliability is of great concern to the scalability of extreme-scale systems. Of particular concern are soft errors in main memory, which are a leading cause of failures on current systems and are predicted to be the leading cause on future systems. While great effort has gone into designing algorithms and applications that can continue to make progress in the presence of these errors without restarting, the most critical software running on a node, the operating system (OS), is currently left relatively unprotected. OS resiliency is of particular importance because, though this software typically represents a small footprint of a compute node's physical memory, recent studies show more memory errors in this region of memory than the remainder of the system. In this paper, we investigate the soft error vulnerability of two operating systems used in current and future high-performance computing systems: Kitten, the lightweight kernel developed at Sandia National Laboratories, and CLE, a high-performance Linux-based operating system developed by Cray. For each of these platforms, we outline major structures and subsystems that are vulnerable to soft errors and describe methods that could be used to reconstruct damaged state. Our results show the Kitten lightweight operating system may be an easier target to harden against memory errors due to its smaller memory footprint, largely deterministic state, and simpler system structure.
Detection of Human, Legitimate Bot, and Malicious Bot in Online Social Networks Based on Wavelets
(2018)

Social interactions take place in environments that influence people’s behaviours and perceptions. Nowadays, the users of Online Social Network (OSN) generate a massive amount of content based on social interactions. However, OSNs wide popularity and ease of access created a perfect scenario to practice malicious activities, compromising their reliability. To detect automatic information broadcast in OSN, we developed a wavelet-based model that classifies users as being human, legitimate robot, or malicious robot, as a result of spectral patterns obtained from users’ textual content. We create the feature vector from the Discrete Wavelet Transform along with a weighting scheme called Lexicon-based Coefficient Attenuation. In particular, we induce a classification model using the Random Forest algorithm over two real Twitter datasets. The corresponding results show the developed model achieved an average accuracy of 94.47% considering two different scenarios: single theme and miscellaneous one.
Postmarketing Drug Safety Surveillance Using Publicly Available Health-Consumer-Contributed Content in Social Media
(2014)

Postmarketing drug safety surveillance is important because many potential adverse drug reactions cannot be identified in the premarketing review process. It is reported that about 5% of hospital admissions are attributed to adverse drug reactions and many deaths are eventually caused, which is a serious concern in public health. Currently, drug safety detection relies heavily on voluntarily reporting system, electronic health records, or relevant databases. There is often a time delay before the reports are filed and only a small portion of adverse drug reactions experienced by health consumers are reported. Given the popularity of social media, many health social media sites are now available for health consumers to discuss any health-related issues, including adverse drug reactions they encounter. There is a large volume of health-consumer-contributed content available, but little effort has been made to harness this information for postmarketing drug safety surveillance to supplement the traditional approach. In this work, we propose the association rule mining approach to identify the association between a drug and an adverse drug reaction. We use the alerts posted by Food and Drug Administration as the gold standard to evaluate the effectiveness of our approach. The result shows that the performance of harnessing health-related social media content to detect adverse drug reaction is good and promising.
Single inductor three-level boost bridgeless PFC rectifier with nature voltage clamp
(2010)

A single inductor three-level boost bridgeless PFC rectifier with nature voltage clamp is proposed. In the proposed converter, the operation principle is quite different from other boost bridgeless three-level converters. The charging stage is presented in the low side while the discharging stage in the high side and vice versa. As a result, nature voltage clamp is achieved. And the slow diodes are ‘shared’. They operate not only as rectifying diodes, but also as voltage clamping diodes. Besides, only single inductor is required and the device utilization factor is high. Besides, the common-mode(CM) noise is rather low. First, the operational principle are analyzed in detail. Then the performance evaluation and design consideration are presented. Finally, a 1kW experimental prototype is built. The result shows that the efficiency is above 94% at full load under 90V and the maximum efficiency is 98.4%.